This paper presents a method for improving the performance of matching systems that correlate using shape templates. The basic idea involves extending an existing set of training shapes with generated "virtual" shapes, in order to improve representational capability, yet no a-priori feature correspondence is necessary among the original shapes in the training set. Instead, an integrated clustering and registration approach partitions the original shape samples into clusters of similar and registered shapes; in each cluster a separate feature space is embedded. This allows the derivation of standard compact parameterizations for each cluster. This paper demonstrates that sampling these low-order spaces can produce an extended training set which facilitates a superior matching performance, as measured by a ROC curve. In the experiments, we consider a realistic application involving thousands of pedestrian shapes and perform correlation matching based on distance transforms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Virtual sample generation for template-based shape matching


    Contributors:
    Gavrila, D.M. (author) / Giebel, J. (author)


    Publication date :

    2001-01-01


    Size :

    633900 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Virtual Sample Generation for Template-Based Shape Matching

    Gavrila, D. M. / Giebel, J. / IEEE | British Library Conference Proceedings | 2001


    Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

    Detry, Renaud / Kulczycki, Eric / Padgett, Curtis et al. | NTRS | 2021


    Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

    Detry, Renaud / Kulczycki, Eric / Padgett, Curtis et al. | NTRS | 2021


    Autonomous Template Generation and Matching for Satellite Constellation Tracking

    Zuehlke, David / Tiwari, Madhur / Henderson, Troy | TIBKAT | 2022


    Autonomous Template Generation and Matching for Satellite Constellation Tracking

    Zuehlke, David / Tiwari, Madhur / Henderson, Troy | AIAA | 2022